Data Scenario and Model Hypothesis

Standard fit report for fits of SISCA to HG_Herring data.

Data Scenario: setup_HG

Model Hypothesis: setup_Aggregate_AR1comps

Species:

Stocks:

Final phase convergence diagnostics

Max Gradient: 0.004894

Objective Function value: 181.6675153

Time to fit model: 0.05

PD Hessian: FALSE

No. of Non-finite SEs: 0

Model fits

At-a-glance

Time series of spawning biomass with scaled spawn indices (top), recruitments (second row), natural mortality (third row), and harvest rates (bottom row) for substocks of HG_Herring. Stocks are, from left to right,Aggregate.

Figure 1: Time series of spawning biomass with scaled spawn indices (top), recruitments (second row), natural mortality (third row), and harvest rates (bottom row) for substocks of HG_Herring. Stocks are, from left to right,Aggregate.

Fits to data

Model fits to spawn indices.

Figure 2: Model fits to spawn indices.

Average model fits to age data. Stocks are left to right, and gears are top to bottom.

Figure 3: Average model fits to age data. Stocks are left to right, and gears are top to bottom.

Model fits to age data, averaged over stock and time. Gears are top to bottom.

Figure 4: Model fits to age data, averaged over stock and time. Gears are top to bottom.

Table 1: Estimated standard deviations for observational data. The first three columns show age data sampling error standard deviations from the logistic-normal compositional likelihood function, and the last column shows spawn survey index standard deviations on the log scale.
\(\tau^{age}_{Red}\) \(\tau^{age}_{SR}\) \(\tau^{age}_{Gn}\) \(\tau^{surv}_{Su}\) \(\tau^{surv}_{D}\)
Aggregate 0.823 0.434 0.649 0.466 0.479

Recruitment

Age-1 recruitments for all stocks. Equilibrium unfished recruitment $R_0$ is indicated by the horizontal dashed line. Second row shows recruitment residuals on the log scale, with the average of estimated residuals shown by the horizontal red dashed line.

Figure 5: Age-1 recruitments for all stocks. Equilibrium unfished recruitment \(R_0\) is indicated by the horizontal dashed line. Second row shows recruitment residuals on the log scale, with the average of estimated residuals shown by the horizontal red dashed line.

Stock-recruit curves (solid lines) and modeled recruitments (coloured points)

Figure 6: Stock-recruit curves (solid lines) and modeled recruitments (coloured points)

Selectivity and Catch

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Figure 7: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Figure 8: Catch in biomass units for each stock (rows). Stacked bars show the total yearly catch for each commercial fleet, and the dead ponded fish shown with a red border.

Selectivity-at-age for each fleet (rows). Aggregate stock average selectivity curves are shown as thick grey lines, while sub-stock specific estimates are shown as dashed thin coloured lines.

Figure 9: Selectivity-at-age for each fleet (rows). Aggregate stock average selectivity curves are shown as thick grey lines, while sub-stock specific estimates are shown as dashed thin coloured lines.

Reference Points

Yield Curves

Equilibrium yield curves as a function of fishing mortality rates, assuming all fishing mortality comes from the gillnet fleet.

Figure 10: Equilibrium yield curves as a function of fishing mortality rates, assuming all fishing mortality comes from the gillnet fleet.

Stock specific fits

Aggregate

Age composition fits

Model fits to yearly Aggregate stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Figure 11: Model fits to yearly Aggregate stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to yearly Aggregate stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Figure 12: Model fits to yearly Aggregate stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to yearly Aggregate stock age compositions for the gillnet fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Figure 13: Model fits to yearly Aggregate stock age compositions for the gillnet fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Age composition residuals for the Aggregate sub-stock. Positive residuals are black black, while negative residuals are red.

Figure 14: Age composition residuals for the Aggregate sub-stock. Positive residuals are black black, while negative residuals are red.

Age composition post tail compression

Model fits to tail compressed yearly Aggregate stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Figure 15: Model fits to tail compressed yearly Aggregate stock age compositions for the reduction fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to tail compressed yearly Aggregate stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Figure 16: Model fits to tail compressed yearly Aggregate stock age compositions for the seineRoe fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Model fits to tail compressed yearly Aggregate stock age compositions for the gillnet fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Figure 17: Model fits to tail compressed yearly Aggregate stock age compositions for the gillnet fleet. Grey bars are age composition observations, and lines/points are the model expected values.

Optimisation performance

Objective function components

Table 2: Objective function components for data observations.
objFun obsSurface obsDive ageRed ageSR ageGill
Total 181.67 -10.48 -6.11 19.86 -26 7.42
Aggregate 181.67 -10.48 -6.11 19.86 -26 7.42
Table 3: Objective function components for standard (single level) and hyper-priors.
V1
objFun 181.670000
recDevs 86.320000
initDevs 9.970000
h 7.930000
M 1.090000
tvMdev 67.370000
IGtau_surf -4.130000
IGtau_dive -4.210000
tvSelAlpha 0.000000
tvSelBeta 0.000000
selAlphaRed 2.850000
selAlphaSR 2.850000
selAlphaGn -0.690000
selBetaRed -1.290000
selBetaSR -1.290000
selBetaGn -0.440000
lnB0 3.243168
lnRinit 5.778798
psiSOK -20.390000
Table 4: Objective function components for hierarchical (mult-level) priors.
V1
objFun 181.67
MDev 0.92
hDev 0.92
selAlphaDevR 0.92
selAlphaDevSR 0.92
selAlphaDevGn 0.92
selBetaDevR 0.92
selBetaDevSR 0.92
selBetaDevGn 0.92

Phase fit table

Table 5: Optimisation performance of SISCA for each phase.
phase objFun maxGrad nPar convCode convMsg time
1 545.6249 0.0000657 3 0 relative convergence (4) 0.0061167
2 544.5749 0.0000884 13 0 relative convergence (4) 0.0040667
3 472.7940 0.0000726 81 0 relative convergence (4) 0.0043000
4 269.6006 0.0001420 146 0 relative convergence (4) 0.0060833
5 269.6006 0.0000722 146 0 relative convergence (4) 0.0040000
6 265.4987 0.0005303 150 0 relative convergence (4) 0.0068333
7 263.7022 0.0006852 153 0 relative convergence (4) 0.0070333
8 263.7022 0.0003487 153 0 relative convergence (4) 0.0040833
9 181.6675 0.0048940 155 0 relative convergence (4) 0.0097167
RE NA NA NA NA NA NA

Leading Parameter SDReport

Table 6: SD report showing leading parameter estimates, standard errors, gradient components, and coefficients of variation. Gradients with a magnitude above 1e-3 are shown in bold red, while the coefficients of variation (cv) are
coloured so that smaller values are lighter in colour, and larger values are darker, with cvs above .5 in bold, and cvs above 3 in red.
est se gr cv
lnB0_p 3.2432 0.0689 -0.007 0.0212
lnRinit_p 5.7788 0.4169 -0.0023 0.0721
logit_ySteepness 0.4934 0.6099 2.4e-05 1.2362
lnM -1.3882 0.2348 0.00073 0.1691
fDevs_ap 0.9699 0.6562 -0.00076 0.6765
fDevs_ap.1 -0.2549 0.6631 -0.00029 2.6016
fDevs_ap.2 0.3603 0.6386 -0.00041 1.7727
fDevs_ap.3 0.4762 0.6439 -0.00027 1.3522
fDevs_ap.4 0.0695 0.6787 -8.1e-05 9.7619
fDevs_ap.5 0.0473 0.7209 -6.6e-05 15.2285
fDevs_ap.6 -0.3239 0.8697 -3.7e-05 2.6854
fDevs_ap.7 -0.2021 0.9166 -2.3e-05 4.5361
fDevs_ap.8 -0.1283 0.9468 -1.6e-05 7.3823
fDevs_ap.9 -0.1542 0.9360 -2.2e-05 6.0702
lnSelAlpha_g 1.0174 0.0649 0.00077 0.0638
lnSelAlpha_g.1 1.3489 0.0430 0.0026 0.0318
lnSelAlpha_g.2 1.6499 0.0430 -0.017 0.026
lnSelBeta_g 0.6489 0.1014 0.00024 0.1563
lnSelBeta_g.1 0.6024 0.0719 0.00013 0.1193
lnSelBeta_g.2 0.2647 0.1144 0.006 0.4324
lntau2Obs_pg -1.5292 0.1973 4.1e-05 0.129
lntau2Obs_pg.1 -1.4707 0.2057 -2.7e-05 0.1399
recDevs_vec 1.2917 0.4671 -0.0018 0.3616
recDevs_vec.1 -1.0435 0.7785 -0.00022 0.7461
recDevs_vec.2 -0.4390 0.4125 -0.00031 0.9395
recDevs_vec.3 -0.7076 0.4533 -0.00017 0.6407
recDevs_vec.4 0.9106 0.3662 -0.00047 0.4021
recDevs_vec.5 -0.0792 0.8295 -0.00014 10.4752
recDevs_vec.6 0.2288 0.6790 -0.00024 2.9673
recDevs_vec.7 0.3971 0.6073 -0.00035 1.5295
recDevs_vec.8 0.0534 0.7658 -0.00028 14.3465
recDevs_vec.9 1.1614 0.5920 -0.00084 0.5097
recDevs_vec.10 0.3537 0.7053 -0.00035 1.994
recDevs_vec.11 -0.9393 0.7045 -3.9e-05 0.75
recDevs_vec.12 -1.6066 0.6703 2.6e-06 0.4172
recDevs_vec.13 -1.2781 0.6782 -4.9e-05 0.5306
recDevs_vec.14 -0.4707 0.5245 -6.6e-05 1.1143
recDevs_vec.15 -0.0392 0.5211 -2.9e-06 13.2795
recDevs_vec.16 0.6240 0.4025 -5e-05 0.645
recDevs_vec.17 0.4506 0.3420 -5.3e-05 0.759
recDevs_vec.18 1.1650 0.2649 -0.00017 0.2274
recDevs_vec.19 1.0518 0.2626 -0.00044 0.2497
recDevs_vec.20 1.1527 0.2561 -0.00074 0.2221
recDevs_vec.21 -0.4422 0.3074 0.002 0.6951
recDevs_vec.22 -0.2750 0.3260 -0.0019 1.1855
recDevs_vec.23 0.2173 0.3191 0.00057 1.4682
recDevs_vec.24 -0.3331 0.3159 -1.9e-05 0.9483
recDevs_vec.25 2.7452 0.2772 0.0017 0.101
recDevs_vec.26 0.5333 0.2779 -0.00017 0.5211
recDevs_vec.27 -0.5640 0.2850 0.00023 0.5053
recDevs_vec.28 -0.7854 0.2923 -0.00031 0.3721
recDevs_vec.29 1.2643 0.2751 -0.00074 0.2176
recDevs_vec.30 0.4142 0.2761 -0.00057 0.6665
recDevs_vec.31 -1.1791 0.3055 -0.00057 0.2591
recDevs_vec.32 -0.7794 0.2671 -0.00039 0.3427
recDevs_vec.33 1.5860 0.2381 -0.00057 0.1501
recDevs_vec.34 0.5273 0.2387 0.00097 0.4527
recDevs_vec.35 -0.3701 0.2474 0.00035 0.6684
recDevs_vec.36 -1.4279 0.2837 4.4e-05 0.1987
recDevs_vec.37 0.5952 0.2617 1e-05 0.4396
recDevs_vec.38 -1.9407 0.3582 -4e-06 0.1846
recDevs_vec.39 -1.9846 0.3652 -0.00025 0.184
recDevs_vec.40 -1.1449 0.3459 -0.00021 0.3021
recDevs_vec.41 0.1271 0.2789 -1.5e-06 2.1943
recDevs_vec.42 0.4162 0.2792 -9.2e-06 0.6708
recDevs_vec.43 1.4833 0.2481 0.00038 0.1672
recDevs_vec.44 -1.7064 0.3059 2.1e-05 0.1793
recDevs_vec.45 -0.5300 0.2922 3e-05 0.5513
recDevs_vec.46 -0.5396 0.3010 -1.7e-05 0.5578
recDevs_vec.47 -0.3611 0.3008 1.6e-05 0.8331
recDevs_vec.48 0.8038 0.2912 5e-05 0.3623
recDevs_vec.49 -0.8037 0.4114 1.9e-07 0.5119
recDevs_vec.50 0.4915 0.3695 2.1e-05 0.7518
recDevs_vec.51 -0.8347 0.4463 6.7e-06 0.5347
recDevs_vec.52 0.7378 0.3888 -1.1e-05 0.527
recDevs_vec.53 -0.9491 0.3941 -1.9e-05 0.4153
recDevs_vec.54 0.7795 0.3463 4.2e-06 0.4442
recDevs_vec.55 -1.1963 0.4211 -1.5e-05 0.352
recDevs_vec.56 0.2639 0.3512 -2e-05 1.3309
recDevs_vec.57 -0.3555 0.3708 -2.5e-05 1.0431
recDevs_vec.58 1.2523 0.3132 -2.5e-05 0.2501
recDevs_vec.59 -0.9032 0.3891 -3.5e-05 0.4308
recDevs_vec.60 -0.5239 0.3840 1.3e-06 0.7329
recDevs_vec.61 -1.0686 0.3875 -1.7e-05 0.3626
omegaM_pt -0.3739 0.9796 4.6e-05 2.6201
omegaM_pt.1 -0.3745 0.9775 5.5e-06 2.6101
omegaM_pt.2 -0.1125 0.9738 -3.3e-05 8.6567
omegaM_pt.3 0.1813 0.9707 -0.00012 5.354
omegaM_pt.4 0.4342 0.9708 -0.00021 2.2359
omegaM_pt.5 0.7556 0.9728 -3e-04 1.2874
omegaM_pt.6 0.6511 0.9711 -0.00032 1.4915
omegaM_pt.7 0.5926 0.9699 -0.00033 1.6365
omegaM_pt.8 0.6635 0.9705 -0.00034 1.4626
omegaM_pt.9 0.6213 0.9714 -0.00035 1.5634
omegaM_pt.10 0.5794 0.9715 -0.00038 1.6768
omegaM_pt.11 0.5128 0.9698 -0.00041 1.8911
omegaM_pt.12 0.5810 0.9655 -0.00048 1.6619
omegaM_pt.13 0.6814 0.9638 -0.00055 1.4144
omegaM_pt.14 0.7708 0.9629 -0.00058 1.2493
omegaM_pt.15 0.5379 0.9670 -0.00057 1.7978
omegaM_pt.16 0.1391 0.9712 -0.00056 6.9835
omegaM_pt.17 -0.0843 0.9709 -0.00055 11.5124
omegaM_pt.18 -0.1292 0.9658 -0.00054 7.4749
omegaM_pt.19 -0.0974 0.9618 -0.00053 9.8748
omegaM_pt.20 -0.1361 0.9604 -0.00052 7.0585
omegaM_pt.21 -0.2982 0.9582 -0.00052 3.2136
omegaM_pt.22 -0.3715 0.9527 -0.00054 2.5643
omegaM_pt.23 -0.2379 0.9458 -6e-04 3.9763
omegaM_pt.24 -0.1186 0.9414 -0.00055 7.9371
omegaM_pt.25 0.0567 0.9386 -6e-04 16.5399
omegaM_pt.26 0.2101 0.9372 -0.00061 4.4598
omegaM_pt.27 0.2440 0.9356 -0.00062 3.8343
omegaM_pt.28 0.2380 0.9363 -0.00054 3.9338
omegaM_pt.29 0.2844 0.9381 -0.00046 3.2985
omegaM_pt.30 0.3004 0.9388 -0.00037 3.1257
omegaM_pt.31 0.3651 0.9376 -3e-04 2.568
omegaM_pt.32 0.4621 0.9360 -0.00027 2.0258
omegaM_pt.33 0.5172 0.9355 -0.00028 1.8086
omegaM_pt.34 0.3741 0.9349 -0.00033 2.4991
omegaM_pt.35 0.0488 0.9354 -0.00041 19.1766
omegaM_pt.36 -0.0319 0.9343 -0.00054 29.28
omegaM_pt.37 -0.1160 0.9344 -0.00059 8.0586
omegaM_pt.38 0.0912 0.9288 -0.00061 10.1801
omegaM_pt.39 0.3082 0.9229 -0.00063 2.9941
omegaM_pt.40 0.4321 0.9249 -0.00061 2.1405
omegaM_pt.41 0.4174 0.9266 -0.00059 2.2199
omegaM_pt.42 0.3860 0.9246 -0.00058 2.3956
omegaM_pt.43 0.3878 0.9254 -0.00059 2.3864
omegaM_pt.44 0.1616 0.9281 -6e-04 5.7429
omegaM_pt.45 0.1011 0.9279 -0.00062 9.1776
omegaM_pt.46 0.3095 0.9232 -0.00061 2.9827
omegaM_pt.47 0.5056 0.9179 -6e-04 1.8154
omegaM_pt.48 0.5623 0.9133 -0.00057 1.6242
omegaM_pt.49 0.5736 0.9145 -0.00055 1.5943
omegaM_pt.50 0.5133 0.9240 -0.00052 1.8001
omegaM_pt.51 0.0807 0.9259 -0.00049 11.4674
omegaM_pt.52 -0.0429 0.9265 -0.00046 21.6181
omegaM_pt.53 -0.1666 0.9252 -0.00043 5.5522
omegaM_pt.54 -0.4362 0.9279 -4e-04 2.1271
omegaM_pt.55 -0.3965 0.9267 -0.00037 2.3369
omegaM_pt.56 -0.3570 0.9279 -0.00034 2.5994
omegaM_pt.57 -0.3126 0.9281 -0.00031 2.9691
omegaM_pt.58 -0.2143 0.9282 -0.00028 4.331
omegaM_pt.59 -0.1416 0.9272 -0.00025 6.5495
omegaM_pt.60 -0.0915 0.9267 -0.00023 10.1317
omegaM_pt.61 0.1025 0.9269 -2e-04 9.0451
omegaM_pt.62 0.2726 0.9301 -0.00018 3.4127
omegaM_pt.63 0.3991 0.9357 -0.00015 2.3442
omegaM_pt.64 0.5263 0.9323 -0.00012 1.7714
omegaM_pt.65 0.0897 0.9487 -9.1e-05 10.5778
omegaM_pt.66 -0.1325 0.9699 -5.9e-05 7.3188
omegaM_pt.67 -0.1939 0.9841 -2.9e-05 5.0752
logitphi1_g -1.0070 0.5812 -2.2e-05 0.5772
logitphi1_g.1 0.5791 0.2386 2.4e-05 0.412
logitphi1_g.2 0.6935 0.4428 -0.0058 0.6384

MCMC posteriors

MCMC performance

## Not Yet Implemented

Other

Compositional Likelihood Correlation Matrices

Estimated correlation matrices for age composition residuals in the reduction fleet. The circles above the visualise the numbers below the diagonal.

Figure 18: Estimated correlation matrices for age composition residuals in the reduction fleet. The circles above the visualise the numbers below the diagonal.

Estimated correlation matrices for age composition residuals in the seineRoe fleet. The circles above the visualise the numbers below the diagonal.

Figure 19: Estimated correlation matrices for age composition residuals in the seineRoe fleet. The circles above the visualise the numbers below the diagonal.

Estimated correlation matrices for age composition residuals in the gillnet fleet. The circles above the visualise the numbers below the diagonal.

Figure 20: Estimated correlation matrices for age composition residuals in the gillnet fleet. The circles above the visualise the numbers below the diagonal.

Compositional Likelihood Diagnostic Plot

Diagnostic plot for compositional likelihood function.

Figure 21: Diagnostic plot for compositional likelihood function.

Comparisons with ISCAM

Plots of average age composition fits at the major stock level. Left is SISCA, right is ISCAM.

Figure 22: Plots of average age composition fits at the major stock level. Left is SISCA, right is ISCAM.

Comparison of spawning stock biomass and age-2 recruitment at the major stock level between ISCAM and SISCA.

Figure 23: Comparison of spawning stock biomass and age-2 recruitment at the major stock level between ISCAM and SISCA.